Science Explorer Interactive view Map

Numerical methods in engineering

Numerical methods in engineering is a research topic within Mechanics of Materials. Science Explorer counts 47k research works in it since 1950. 21.1% of them reached the world's top 10% most cited for their field and year.

This cluster of papers represents advances in fracture mechanics modeling and simulation, focusing on topics such as fracture, meshless methods, extended finite element method, peridynamics, phase-field modeling, radial basis functions, crack propagation, brittle materials, discontinuities, and structural mechanics.

  • Fracture
  • Meshless Methods
  • Extended Finite Element Method
  • Peridynamics
  • Phase-Field Modeling
  • Radial Basis Functions
  • Crack Propagation
  • Brittle Materials
  • Discontinuities
  • Structural Mechanics
Research works
47k
fractional, since 1950
In the world top 10%
9.8k
per year above
Top-10% rate
21.1%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+15%
the tick is no change

Which countries lead Numerical methods in engineering research?

By volume, China and the United States publish the most (2.3k and 582 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.3k works
  2. 2 United States 582 works
  3. 3 India 522 works
  4. 4 Germany 327 works
  5. 5 Iran 269 works
  6. 6 France 258 works
  7. 7 Italy 225 works
  8. 8 Russia 140 works
  9. 9 United Kingdom 134 works
  10. 10 Türkiye 134 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 46.7%United States: 12.0%India: 10.7%Germany: 6.7%6 others listed: 23.9%47%largest
China2,273 · 46.7%United States582 · 12.0%India522 · 10.7%Germany327 · 6.7%6 others listed1,160 · 23.9%

Shares of the rows listed above, not of the whole node.

Which institutions lead Numerical methods in engineering research?

By volume in 2022–2025, Dalian University of Technology publishes the most Numerical methods in engineering research, followed by Tongji University and Hohai University.

Who are the leading researchers in Numerical methods in engineering?

The most-cited researchers publishing on Numerical methods in engineering include Xiaogang Wang, Ted Belytschko and Thomas J.R. Hughes.

  1. 1 Xiaogang Wang Russia 13k citations
  2. 2 Ted Belytschko United States 4.1k citations
  3. 3 Thomas J.R. Hughes United States 3.7k citations
  4. 4 John W. Hutchinson United States 3.2k citations
  5. 5 A.G. Evans United States 3.2k citations
  6. 6 J. N. Reddy United States 2.9k citations
  7. 7 Zdeněk P. Bažant United States 2.8k citations
  8. 8 Timon Rabczuk Germany 2.7k citations
  9. 9 Sia Nemat‐Nasser United States 2.7k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Numerical methods in engineering research done?

The largest centres of Numerical methods in engineering research in 2022–2025 are Beijing (China), Nanjing (China), Shanghai (China) and Xi'an (China). Among places with at least 20 works in it, it is an unusually large share of all research in Dhanbad.

Largest cities, 2022–2025

  1. 1 Beijing China 322 works
  2. 2 Nanjing China 198 works
  3. 3 Shanghai China 188 works
  4. 4 Xi'an China 122 works
  5. 5 Tehran Iran 100 works
  6. 6 Wuhan China 94 works
  7. 7 Chengdu China 84 works
  8. 8 Dalian China 82 works
  9. 9 Paris France 81 works
  10. 10 Harbin China 76 works

Where it is the local speciality

  1. DhanbadIN · 25.1 works18×
← less than its size predictsmore →

Location quotient: how much more of its research is in Numerical methods in engineering than the world average.

See Numerical methods in engineering on the map

Where is the best place to study Numerical methods in engineering?

Among universities, judged by research, Hohai University, Dalian University of Technology and Bauhaus-Universität Weimar score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 28.07%fractional works in this node (log) →share in the world top 10% →Hohai University: 67, 29.5%Dalian University of Technology: 70, 22.8%Bauhaus-Universität Weimar: 13, 37.0%Leibniz University Hannover: 32, 26.0%Tongji University: 69, 25.4%Jouf University: 10, 27.6%Hong Kong Polytechnic University: 25, 34.5%Indian Institute of Technology Dhanbad: 25, 14.7%Chongqing Normal University: 9, 36.9%University of Calabria: 13, 26.3%Bauhaus-Universität …Hohai UniversityLeibniz University H…Dalian University of…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Hohai UniversityChina 75.229.5%18.7×67 +39.5%
2 Dalian University of TechnologyChina 68.322.8%11.4×70 +14.7%
3 Bauhaus-Universität WeimarGermany 67.737.0%46.1×13 +47.1%
4 Leibniz University HannoverGermany 66.826.0%17.2×32 +41.8%
5 Tongji UniversityChina 66.525.4%8.3×69 +21.4%
6 Jouf UniversitySaudi Arabia 64.027.6%11.3×10
7 Hong Kong Polytechnic UniversityHong Kong 63.734.5%4.4×25 +69.2%
8 Indian Institute of Technology DhanbadIndia 63.214.7%18.8×25 +278.0%
9 Chongqing Normal UniversityChina 62.336.9%10.3×9 +60.6%
10 University of CalabriaItaly 57.726.3%10.1×13 +26.3%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Numerical methods in engineering research growing?

Output in 2018–2022 was 15% higher than in 2013–2017, peaking in 2020. The fastest-growing topics are Numerical methods in engineering.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.